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Optical Engineering

Improved saliency toolbox/Itti model for region of interest extraction
Author(s): Dong-Jian He; Shang-Wang Liu; Xin-Hong Liang; Cheng Cai
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Paper Abstract

The saliency toolbox (STB)/Itti model is an outstanding computational selective visual attention model. In this paper, we propose an improved STB/Itti model to overcome the drawback of STB/Itti-its output "saliency map" is not large enough for region of interest (ROI) extraction. First, we employ a simplified pulse coupled neural network (PCNN) with a special input image, and more importantly, the PCNN does not require iterations. Subsequently, the PCNN takes the place of the winner-take-all network in STB/Itti. Experimental results show that the improved STB/Itti model works well for ROI extraction, with the mean area under the curve value of 0.8306 and robustness against noise and geometric attacks. The proposed model can greatly enhance the performances of both STB/Itti and PCNN model in image processing.

Paper Details

Date Published: 1 September 2011
PDF: 14 pages
Opt. Eng. 50(9) 097202 doi: 10.1117/1.3625422
Published in: Optical Engineering Volume 50, Issue 9
Show Author Affiliations
Dong-Jian He, Northwest A&F Univ. (China)
Shang-Wang Liu, Northwest A&F Univ. (China)
Xin-Hong Liang, Henan Institute of Science and Technology (China)
Cheng Cai, Northwest A&F Univ. (China)


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